SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5401–5425 of 10420 papers

TitleStatusHype
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment AnalysisCode0
PanorAMS: Automatic Annotation for Detecting Objects in Urban Context—0
Weakly Supervised Faster-RCNN+FPN to classify animals in camera trap images—0
An efficient and flexible inference system for serving heterogeneous ensembles of deep neural networks—0
Robustness and invariance properties of image classifiers—0
Probing Contextual Diversity for Dense Out-of-Distribution DetectionCode0
An Access Control Method with Secret Key for Semantic Segmentation Models—0
Dense Depth Distillation with Out-of-Distribution Simulated Images—0
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model—0
Calibrated Selective ClassificationCode0
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve InterpretabilityCode0
TMIC: App Inventor Extension for the Deployment of Image Classification Models Exported from Teachable Machine—0
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window—0
Time-lapse image classification using a diffractive neural network—0
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling—0
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed LearningCode0
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning—0
Multilayer deep feature extraction for visual texture recognition—0
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language TasksCode0
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification—0
GCISG: Guided Causal Invariant Learning for Improved Syn-to-real Generalization—0
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning—0
Revisiting ensembling for improving the performance of deep learning models—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified